Introducing a pythonic CLI

examples/entrypoint/experiment.py

@@ -0,0 +1,107 @@
+from dataclasses import dataclass
+from typing import List
+
+import nemo_run as run
+
+
+@dataclass
+class Model:
+    """Dummy model config"""
+
+    hidden_size: int
+    num_layers: int
+    activation: str
+
+
+@dataclass
+class Optimizer:
+    """Dummy optimizer config"""
+
+    learning_rate: float
+    weight_decay: float
+    betas: List[float]
+
+
+@run.cli.entrypoint
+def train_model(model: Model, optimizer: Optimizer, epochs: int = 10, batch_size: int = 32):
+    """
+    Train a model using the specified configuration.
+
+    Args:
+        model (Model): Configuration for the model.
+        optimizer (Optimizer): Configuration for the optimizer.
+        epochs (int, optional): Number of training epochs. Defaults to 10.
+        batch_size (int, optional): Batch size for training. Defaults to 32.
+    """
+    print("Training model with the following configuration:")
+    print(f"Model: {model}")
+    print(f"Optimizer: {optimizer}")
+    print(f"Epochs: {epochs}")
+    print(f"Batch size: {batch_size}")
+
+    # Simulating model training
+    for epoch in range(epochs):
+        print(f"Epoch {epoch + 1}/{epochs}")
+
+    print("Training completed!")
+
+
+@run.cli.factory
+@run.autoconvert
+def my_model(hidden_size: int = 256, num_layers: int = 3, activation: str = "relu") -> Model:
+    """
+    Create a model configuration.
+    """
+    return Model(hidden_size=hidden_size, num_layers=num_layers, activation=activation)
+
+
+@run.cli.factory
+@run.autoconvert
+def my_optimizer(
+    learning_rate: float = 0.001, weight_decay: float = 1e-5, betas: List[float] = [0.9, 0.999]
+) -> Optimizer:
+    """
+    Create an optimizer configuration.
+    """
+    return Optimizer(learning_rate=learning_rate, weight_decay=weight_decay, betas=betas)
+
+
+@run.cli.factory
+@run.autoconvert
+def local_executor() -> run.LocalExecutor:
+    return run.LocalExecutor()
+
+
+@run.cli.entrypoint(type="experiment")
+def train_models_experiment(
+    ctx: run.cli.RunContext,
+    models: List[Model] = [my_model(), my_model(hidden_size=512)],
+    optimizers: List[Optimizer] = [my_optimizer(), my_optimizer(learning_rate=0.01)],
+    epochs: int = 10,
+    batch_size: int = 32,
+    sequential: bool = False,
+):
+    """
+    Run an experiment to train multiple models with different configurations.
+
+    Args:
+        ctx (run.RunContext): The run context for the experiment.
+        models (List[Model]): List of model configurations to train.
+        optimizers (List[Optimizer]): List of optimizer configurations to use.
+        epochs (int): Number of training epochs for each model.
+        batch_size (int): Batch size for training.
+    """
+
+    with run.Experiment("train_models_experiment") as exp:
+        for i, (model, optimizer) in enumerate(zip(models, optimizers)):
+            train = run.Partial(
+                train_model, model=model, optimizer=optimizer, epochs=epochs, batch_size=batch_size
+            )
+
+            exp.add(train, name=f"train_model_{i}", executor=ctx.executor)
+
+        ctx.launch(exp, sequential=sequential)
+
+
+if __name__ == "__main__":
+    run.cli.main(train_models_experiment)

examples/entrypoint/task.py

@@ -0,0 +1,73 @@
+from dataclasses import dataclass
+from typing import List
+
+import nemo_run as run
+
+
+@dataclass
+class Model:
+    """Dummy model config"""
+
+    hidden_size: int
+    num_layers: int
+    activation: str
+
+
+@dataclass
+class Optimizer:
+    """Dummy optimizer config"""
+
+    learning_rate: float
+    weight_decay: float
+    betas: List[float]
+
+
+@run.cli.factory
+@run.autoconvert
+def my_model(hidden_size: int = 256, num_layers: int = 3, activation: str = "relu") -> Model:
+    """
+    Create a model configuration.
+    """
+    return Model(hidden_size=hidden_size, num_layers=num_layers, activation=activation)
+
+
+@run.cli.factory
+def my_optimizer(
+    learning_rate: float = 0.001, weight_decay: float = 1e-5, betas: List[float] = [0.9, 0.999]
+) -> run.Config[Optimizer]:
+    """Create an optimizer configuration."""
+    return run.Config(
+        Optimizer, learning_rate=learning_rate, weight_decay=weight_decay, betas=betas
+    )
+
+
+def train_model(
+    model: Model,
+    optimizer: Optimizer,
+    epochs: int = 10,
+    batch_size: int = 32,
+):
+    """
+    Train a model using the specified configuration.
+
+    Args:
+        model (Model): Configuration for the model.
+        optimizer (Optimizer): Configuration for the optimizer.
+        epochs (int, optional): Number of training epochs. Defaults to 10.
+        batch_size (int, optional): Batch size for training. Defaults to 32.
+    """
+    print("Training model with the following configuration:")
+    print(f"Model: {model}")
+    print(f"Optimizer: {optimizer}")
+    print(f"Epochs: {epochs}")
+    print(f"Batch size: {batch_size}")
+
+    # Simulating model training
+    for epoch in range(epochs):
+        print(f"Epoch {epoch + 1}/{epochs}")
+
+    print("Training completed!")
+
+
+if __name__ == "__main__":
+    run.cli.main(train_model)

examples/entrypoint/task_with_defaults.py

@@ -0,0 +1,98 @@
+from dataclasses import dataclass
+from typing import List
+
+import nemo_run as run
+
+
+@dataclass
+class Model:
+    """Dummy model config"""
+
+    hidden_size: int
+    num_layers: int
+    activation: str
+
+
+@dataclass
+class Optimizer:
+    """Dummy optimizer config"""
+
+    learning_rate: float
+    weight_decay: float
+    betas: List[float]
+
+
+@run.cli.factory
+@run.autoconvert
+def my_model(hidden_size: int = 256, num_layers: int = 3, activation: str = "relu") -> Model:
+    """
+    Create a model configuration.
+    """
+    return Model(hidden_size=hidden_size, num_layers=num_layers, activation=activation)
+
+
+@run.cli.factory
+def my_optimizer(
+    learning_rate: float = 0.001, weight_decay: float = 1e-5, betas: List[float] = [0.9, 0.999]
+) -> run.Config[Optimizer]:
+    """Create an optimizer configuration."""
+    return run.Config(
+        Optimizer, learning_rate=learning_rate, weight_decay=weight_decay, betas=betas
+    )
+
+
+def train_model(
+    model: Model,
+    optimizer: Optimizer,
+    epochs: int = 10,
+    batch_size: int = 32,
+):
+    """
+    Train a model using the specified configuration.
+
+    Args:
+        model (Model): Configuration for the model.
+        optimizer (Optimizer): Configuration for the optimizer.
+        epochs (int, optional): Number of training epochs. Defaults to 10.
+        batch_size (int, optional): Batch size for training. Defaults to 32.
+    """
+    print("Training model with the following configuration:")
+    print(f"Model: {model}")
+    print(f"Optimizer: {optimizer}")
+    print(f"Epochs: {epochs}")
+    print(f"Batch size: {batch_size}")
+
+    # Simulating model training
+    for epoch in range(epochs):
+        print(f"Epoch {epoch + 1}/{epochs}")
+
+    print("Training completed!")
+
+
+def custom_defaults() -> run.Partial[train_model]:
+    return run.Partial(
+        train_model,
+        model=my_model(hidden_size=512),
+        optimizer=my_optimizer(learning_rate=0.0005),
+        epochs=50,
+        batch_size=2048,
+    )
+
+
+@run.autoconvert
+def local_executor() -> run.Executor:
+    return run.LocalExecutor()
+
+
+class DummyPlugin(run.Plugin):
+    def setup(self, task: run.Partial[train_model], executor: run.Executor):
+        task.epochs *= 2
+
+
+if __name__ == "__main__":
+    run.cli.main(
+        train_model,
+        default_factory=custom_defaults,
+        default_executor=local_executor(),
+        default_plugins=run.Config(DummyPlugin),
+    )

pyproject.toml

@@ -23,7 +23,6 @@ dependencies = [
     "catalogue>=2.0.10",
     "fabric>=3.2.2",
     "fiddle>=0.3.0",
-    "lark>=1.1.9",
     "torchx>=0.7.0",
     "typer>=0.12.3",
     "rich>=13.7.1",

requirements-dev.lock

@@ -7,11 +7,12 @@
 #   all-features: false
 #   with-sources: false
 #   generate-hashes: false
-#   universal: false
 
 -e file:.
 absl-py==2.1.0
     # via fiddle
+appnope==0.1.4
+    # via ipykernel
 asttokens==2.4.1
     # via stack-data
 attrs==24.2.0

requirements.lock

@@ -7,7 +7,6 @@
 #   all-features: false
 #   with-sources: false
 #   generate-hashes: false
-#   universal: false
 
 -e file:.
 absl-py==2.1.0

src/nemo_run/__init__.py

@@ -13,14 +13,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from nemo_run.api import autoconvert, list_tasks, task
+from nemo_run import cli
+from nemo_run.api import autoconvert, dryrun_fn
 from nemo_run.config import Config, Partial, Script
-from nemo_run.core.execution.base import (
-    Executor,
-    ExecutorMacros,
-    FaultTolerance,
-    Torchrun,
-)
+from nemo_run.core.execution.base import Executor, ExecutorMacros, FaultTolerance, Torchrun
 from nemo_run.core.execution.local import LocalExecutor
 from nemo_run.core.execution.skypilot import SkypilotExecutor
 from nemo_run.core.execution.slurm import SlurmExecutor
@@ -35,6 +31,8 @@
 
 __all__ = [
     "autoconvert",
+    "cli",
+    "dryrun_fn",
     "Config",
     "DevSpace",
     "Executor",
@@ -43,7 +41,6 @@
     "FaultTolerance",
     "GitArchivePackager",
     "help",
-    "list_tasks",
     "LocalExecutor",
     "LocalTunnel",
     "Packager",
@@ -54,7 +51,6 @@
     "SkypilotExecutor",
     "SlurmExecutor",
     "SSHTunnel",
-    "task",
     "Torchrun",
 ]